Abstract A major approach in causal inference literature aimed at mitigating bias due to unmeasured confounding is the so- called instrumental variable (IV) design which relies on identifying a variable which (i) influences the treatment process, (ii) has no direct effect on the outcome other than through the treatment, and (iii) is independent of any unmeasured confounder. IV methods are very well developed and widely used in social and health science, although validity of IV inferences may not be reliable if any of required assumptions (i)-(iii) is violated. This proposal aims to develop (a) new IV methods robust to violation of any of (i)-(iii); (b) New negative control methods that can be used to detect and sometimes to nonparametrically account for unmeasured confounding bias; (c) New bracketing methods for partial inference about causal effects in comparative interrupted time series studies. The proposed methods will be used to address current scientific queries in three major substantive public health areas:(1) to understand the health effects of air pollution; (2) to quantify the causal effects of modifiable risk factors for Alzheimer's disease and related disorders; (3) To uncover the mechanism by which a randomized package of interventions produced a substantial reduction of HIV incidence in a recent major cluster randomized trial of treatment as prevention in Botswana, Africa. Our proposal will provide the best available analytical methods to date to resolve confounding concerns in these high impact public health applications and more broadly in observational studies in the health sciences.